Multimodal named entity recognition

ABSTRACT

A caption of a multimodal message (e.g., social media post) can be identified as a named entity using an entity recognition system. The entity recognition system can use an attention-based mechanism that emphasis or de-emphasizes each data type (e.g., image, word, character) in the multimodal message based on each datatypes relevance. The output of the attention mechanism can be used to update a recurrent network to identify one or more words in the caption as being a named entity.

CLAIM OF PRIORITY

This application is a continuation of U.S. patent application Ser. No.16/125,615, filed on Sep. 7, 2018, which claims the benefit of priorityof U.S. Provisional Application Ser. No. 62/556,206, filed on Sep. 8,2017, each of which are hereby incorporated by reference herein in theirentireties.

TECHNICAL FIELD

Embodiments of the present disclosure relate generally to machinelearning and, more particularly, but not by way of limitation, toidentifying named entities using machine learning.

BACKGROUND

Information extraction is a computational task in which one or moreterms are extracted from a set of data (e.g., a sentence). For example,a user with a voice recognition-enabled dictation device (e.g., a laptopwith a microphone and dictation software) can say out-loud, “I travelledto Noe Valley”, and the device may use an information extraction schemeto detect that “Noe Valley” (a neighborhood in San Francisco) is thecorrect entity being discussed instead of “no valley”. Informationextraction schemes work well when the terms to be detected are inputcorrectly without errors (e.g., typed correctly, pronounced correctly).It can be difficult for information extraction schemes to extract termson sparse noisy data, such as a social media post consisting of a fewwords, some of which may be misspelled.

BRIEF DESCRIPTION OF THE DRAWINGS

To easily identify the discussion of any particular element or act, themost significant digit or digits in a reference number refer to thefigure (“FIG.”) number in which that element or act is first introduced.

FIG. 1 is a block diagram showing an example messaging system forexchanging data (e.g., messages and associated content) over a network.

FIG. 2 is block diagram illustrating further details regarding amessaging system having an integrated virtual object machine learningsystem, according to example embodiments.

FIG. 3 is a schematic diagram illustrating data that may be stored in adatabase of a messaging server system, according to certain exampleembodiments.

FIG. 4 is a schematic diagram illustrating a structure of a message,according to some embodiments, generated by a messaging clientapplication for communication.

FIG. 5 is a schematic diagram illustrating an example access-limitingprocess, in terms of which access to content (e.g., an ephemeralmessage, and associated multimedia payload of data) or a contentcollection (e.g., an ephemeral message story) may be time-limited (e.g.,made ephemeral).

FIG. 6 shows an example entity recognition system, according to someexample embodiments.

FIG. 7 shows a flow diagram of a method for identifying named entityfrom a multimodal message, according to some example embodiments.

FIG. 8 shows example inputs and results of multimodal messages,according to some example embodiments.

FIG. 9 shows example architecture of an entity recognition system,according to some example embodiments.

FIGS. 10 and 11 show example multimodal messages implementing an entityrecognition system, according to some example embodiments.

FIG. 12 is a block diagram illustrating a representative softwarearchitecture, which may be used in conjunction with various hardwarearchitectures herein described.

FIG. 13 is a block diagram illustrating components of a machine,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of variousembodiments of the inventive subject matter. It will be evident,however, to those skilled in the art, that embodiments of the inventivesubject matter may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail.

Named Entity Recognition (NER) is an information extraction scheme thatattempts to identify that one or more words of text correspond to anamed entity. For example, an NER scheme may attempt to identify theterm “monopoly” not as a reference to a company cornering a market, butrather as the named board game Monopoly®. Further, an NER scheme may betrained to recognize three words as a city “New York City” (a city inAmerica), instead of just recognizing the city as “York” (a city inEngland). NER schemes work well when trained using a largewell-structured dataset. However, it is difficult for an NER scheme torecognize a named entity from a few words, some of which may beintentionally misspelled (e.g., a caption in a social media post: “I luvnew yoooooork ctiy!”).

A multimodal approach for named entity recognition (NER) task from noisyuser generated data, such as social media posts, is disclosed. In theexamples discussed below, the social media posts can include a shorttext caption with or without an accompanying image. The social mediaposts often use inconsistent or incomplete syntax and lexical notations(e.g., misspelled words, images without metadata tags, such as an imageof a city with no location tag), thus overwhelmingly many unknown wordtokens may exist, which causes NER to be impractical if not impossible.

To this end, an NER system implements bi-directional long short-termmemory unit (Bi-LSTM) word/character based NER model with (1) arecurrent neural network module which incorporates relevant visualcontext to augment textual information, and (2) a modality attentionmodule which attenuates irrelevant or uninformative modalities whilefocusing on the primary modality to extract contexts adaptive to eachsample and token.

FIG. 1 is a block diagram showing an example messaging system 100 forexchanging data (e.g., messages and associated content) over a network.The messaging system 100 includes multiple client devices 102, each ofwhich hosts a number of applications including a messaging clientapplication 104. Each messaging client application 104 iscommunicatively coupled to other instances of the messaging clientapplication 104 and a messaging server system 108 via a network 106(e.g., the Internet).

Accordingly, each messaging client application 104 is able tocommunicate and exchange data with another messaging client application104 and with the messaging server system 108 via the network 106. Thedata exchanged between messaging client applications 104, and between amessaging client application 104 and the messaging server system 108,includes functions (e.g., commands to invoke functions) as well aspayload data (e.g., text, audio, video, or other multimedia data).

The messaging server system 108 provides server-side functionality viathe network 106 to a particular messaging client application 104. Whilecertain functions of the messaging system 100 are described herein asbeing performed by either a messaging client application 104 or by themessaging server system 108, it will be appreciated that the location ofcertain functionality within either the messaging client application 104or the messaging server system 108 is a design choice. For example, itmay be technically preferable to initially deploy certain technology andfunctionality within the messaging server system 108, and to latermigrate this technology and functionality to the messaging clientapplication 104 where a client device 102 has a sufficient processingcapacity.

The messaging server system 108 supports various services and operationsthat are provided to the messaging client application 104. Suchoperations include transmitting data to, receiving data from, andprocessing data generated by the messaging client application 104. Thisdata may include message content, client device information, geolocationinformation, media annotation and overlays, message content persistenceconditions, social network information, and live event information, asexamples. Data exchanges within the messaging system 100 are invoked andcontrolled through functions available via user interfaces (UIs) of themessaging client application 104.

Turning now specifically to the messaging server system 108, anapplication programming interface (API) server 110 is coupled to, andprovides a programmatic interface to, an application server 112. Theapplication server 112 is communicatively coupled to a database server118, which facilitates access to a database 120 in which is stored dataassociated with messages processed by the application server 112.

The API server 110 receives and transmits message data (e.g., commandsand message payloads) between the client devices 102 and the applicationserver 112. Specifically, the API server 110 provides a set ofinterfaces (e.g., routines and protocols) that can be called or queriedby the messaging client application 104 in order to invoke functionalityof the application server 112. The API server 110 exposes variousfunctions supported by the application server 112, including accountregistration; login functionality; the sending of messages, via theapplication server 112, from a particular messaging client application104 to another messaging client application 104; the sending of mediafiles (e.g., images or video) from a messaging client application 104 toa messaging server application 114 for possible access by anothermessaging client application 104; the setting of a collection of mediadata (e.g., a story); the retrieval of such collections; the retrievalof a list of friends of a user of a client device 102; the retrieval ofmessages and content; the adding and deletion of friends to and from asocial graph; the location of friends within the social graph; andopening application events (e.g., relating to the messaging clientapplication 104).

The application server 112 hosts a number of applications andsubsystems, including the messaging server application 114, an imageprocessing system 116, and a social network system 122. The messagingserver application 114 implements a number of message-processingtechnologies and functions particularly related to the aggregation andother processing of content (e.g., textual and multimedia content)included in messages received from multiple instances of the messagingclient application 104. As will be described in further detail, the textand media content from multiple sources may be aggregated intocollections of content (e.g., called stories or galleries). Thesecollections are then made available, by the messaging server application114, to the messaging client application 104. Other processor- andmemory-intensive processing of data may also be performed server-side bythe messaging server application 114, in view of the hardwarerequirements for such processing.

The application server 112 also includes the image processing system116, which is dedicated to performing various image processingoperations, typically with respect to images or video received withinthe payload of a message at the messaging server application 114.

The social network system 122 supports various social networkingfunctions and services, and makes these functions and services availableto the messaging server application 114. To this end, the social networksystem 122 maintains and accesses an entity graph (e.g., entity graph304 in FIG. 3) within the database 120. Examples of functions andservices supported by the social network system 122 include theidentification of other users of the messaging system 100 with whom aparticular user has relationships or whom the particular user is“following,” and also the identification of other entities and interestsof a particular user.

The application server 112 is communicatively coupled to a databaseserver 118, which facilitates access to a database 120 in which isstored data associated with messages processed by the messaging serverapplication 114.

FIG. 2 is block diagram illustrating further details regarding themessaging system 100, according to example embodiments. Specifically,the messaging system 100 is shown to comprise the messaging clientapplication 104 and the application server 112, which in turn embody anumber of subsystems, namely an ephemeral timer system 202, a collectionmanagement system 204, an annotation system 206, and an entityrecognition system 210.

The ephemeral timer system 202 is responsible for enforcing thetemporary access to content permitted by the messaging clientapplication 104 and the messaging server application 114. To this end,the ephemeral timer system 202 incorporates a number of timers that,based on duration and display parameters associated with a message orcollection of messages (e.g., a story), selectively display and enableaccess to messages and associated content via the messaging clientapplication 104. Further details regarding the operation of theephemeral timer system 202 are provided below.

The collection management system 204 is responsible for managingcollections of media (e.g., collections of text, image, video, and audiodata). In some examples, a collection of content (e.g., messages,including images, video, text, and audio) may be organized into an“event gallery” or an “event story.” Such a collection may be madeavailable for a specified time period, such as the duration of an eventto which the content relates. For example, content relating to a musicconcert may be made available as a “story” for the duration of thatmusic concert. The collection management system 204 may also beresponsible for publishing an icon that provides notification of theexistence of a particular collection to the user interface of themessaging client application 104.

The collection management system 204 furthermore includes a curationinterface 208 that allows a collection manager to manage and curate aparticular collection of content. For example, the curation interface208 enables an event organizer to curate a collection of contentrelating to a specific event (e.g., delete inappropriate content orredundant messages). Additionally, the collection management system 204employs machine vision (or image recognition technology) and contentrules to automatically curate a content collection. In certainembodiments, compensation may be paid to a user for inclusion ofuser-generated content into a collection. In such cases, the curationinterface 208 operates to automatically make payments to such users forthe use of their content.

The annotation system 206 provides various functions that enable a userto annotate or otherwise modify or edit media content associated with amessage. For example, the annotation system 206 provides functionsrelated to the generation and publishing of media overlays for messagesprocessed by the messaging system 100. The annotation system 206operatively supplies a media overlay (e.g., a filter) to the messagingclient application 104 based on a geolocation of the client device 102.In another example, the annotation system 206 operatively supplies amedia overlay to the messaging client application 104 based on otherinformation, such as social network information of the user of theclient device 102. A media overlay may include audio and visual contentand visual effects. Examples of audio and visual content includepictures, text, logos, animations, and sound effects. An example of avisual effect includes color overlaying. The audio and visual content orthe visual effects can be applied to a media content item (e.g., aphoto) at the client device 102. For example, the media overlay includestext that can be overlaid on top of a photograph generated by the clientdevice 102. In another example, the media overlay includes anidentification of a location (e.g., Venice Beach), a name of a liveevent, or a name of a merchant (e.g., Beach Coffee House). In anotherexample, the annotation system 206 uses the geolocation of the clientdevice 102 to identify a media overlay that includes the name of amerchant at the geolocation of the client device 102. The media overlaymay include other indicia associated with the merchant. The mediaoverlays may be stored in the database 120 and accessed through thedatabase server 118.

In one example embodiment, the annotation system 206 provides auser-based publication platform that enables users to select ageolocation on a map and upload content associated with the selectedgeolocation. The user may also specify circumstances under whichparticular content should be offered to other users. The annotationsystem 206 generates a media overlay that includes the uploaded contentand associates the uploaded content with the selected geolocation.

In another example embodiment, the annotation system 206 provides amerchant-based publication platform that enables merchants to select aparticular media overlay associated with a geolocation via a biddingprocess. For example, the annotation system 206 associates the mediaoverlay of a highest-bidding merchant with a corresponding geolocationfor a predefined amount of time.

The entity recognition system 210 comprises one or more neural networksconfigured to identify an entity referenced by a multimodal message, asdiscussed in further detail below.

FIG. 3 is a schematic diagram illustrating data 300 which may be storedin the database 120 of the messaging server system 108, according tocertain example embodiments. While the content of the database 120 isshown to comprise a number of tables, it will be appreciated that thedata could be stored in other types of data structures (e.g., as anobject-oriented database).

The database 120 includes message data stored within a message table314. An entity table 302 stores entity data, including an entity graph304. Entities for which records are maintained within the entity table302 may include individuals, corporate entities, organizations, objects,places, events, and so forth. Regardless of type, any entity regardingwhich the messaging server system 108 stores data may be a recognizedentity. Each entity is provided with a unique identifier, as well as anentity type identifier (not shown).

The entity graph 304 furthermore stores information regardingrelationships and associations between or among entities. Suchrelationships may be social, professional (e.g., work at a commoncorporation or organization), interest-based, or activity-based, forexample.

The database 120 also stores annotation data, in the example form offilters, in an annotation table 312. Filters for which data is storedwithin the annotation table 312 are associated with and applied tovideos (for which data is stored in a video table 310) and/or images(for which data is stored in an image table 308). Filters, in oneexample, are overlays that are displayed as overlaid on an image orvideo during presentation to a recipient user. Filters may be of varioustypes, including user-selected filters from a gallery of filterspresented to a sending user by the messaging client application 104 whenthe sending user is composing a message. Other types of filters includegeolocation filters (also known as geo-filters), which may be presentedto a sending user based on geographic location. For example, geolocationfilters specific to a neighborhood or special location may be presentedwithin a user interface by the messaging client application 104, basedon geolocation information determined by a Global Positioning System(GPS) unit of the client device 102. Another type of filter is a datafilter, which may be selectively presented to a sending user by themessaging client application 104, based on other inputs or informationgathered by the client device 102 during the message creation process.Examples of data filters include a current temperature at a specificlocation, a current speed at which a sending user is traveling, abattery life for a client device 102, or the current time.

Other annotation data that may be stored within the image table 308 isso-called “lens” data. A “lens” may be a real-time special effect andsound that may be added to an image or a video.

As mentioned above, the video table 310 stores video data which, in oneembodiment, is associated with messages for which records are maintainedwithin the message table 314. Similarly, the image table 308 storesimage data associated with messages for which message data is stored inthe message table 314. The entity table 302 may associate variousannotations from the annotation table 312 with various images and videosstored in the image table 308 and the video table 310.

A story table 306 stores data regarding collections of messages andassociated image, video, or audio data, which are compiled into acollection (e.g., a story or a gallery). The creation of a particularcollection may be initiated by a particular user (e.g., each user forwhom a record is maintained in the entity table 302). A user may createa “personal story” in the form of a collection of content that has beencreated and sent/broadcast by that user. To this end, the user interfaceof the messaging client application 104 may include an icon that isuser-selectable to enable a sending user to add specific content to hisor her personal story.

A collection may also constitute a “live story,” which is a collectionof content from multiple users that is created manually, automatically,or using a combination of manual and automatic techniques. For example,a “live story” may constitute a curated stream of user-submitted contentfrom various locations and events. Users whose client devices 102 havelocation services enabled and are at a common location or event at aparticular time may, for example, be presented with an option, via auser interface of the messaging client application 104, to contributecontent to a particular live story. The live story may be identified tothe user by the messaging client application 104 based on his or herlocation. The end result is a “live story” told from a communityperspective.

A further type of content collection is known as a “location story,”which enables a user whose client device 102 is located within aspecific geographic location (e.g., on a college or university campus)to contribute to a particular collection. In some embodiments, acontribution to a location story may require a second degree ofauthentication to verify that the end user belongs to a specificorganization or other entity (e.g., is a student on the universitycampus).

FIG. 4 is a schematic diagram illustrating a structure of a message 400,according to some embodiments, generated by a messaging clientapplication 104 for communication to a further messaging clientapplication 104 or the messaging server application 114. The content ofa particular message 400 is used to populate the message table 314stored within the database 120, accessible by the messaging serverapplication 114. Similarly, the content of a message 400 is stored inmemory as “in-transit” or “in-flight” data of the client device 102 orthe application server 112. The message 400 is shown to include thefollowing components:

-   -   A message identifier 402: a unique identifier that identifies        the message 400.    -   A message text payload 404: text, to be generated by a user via        a user interface of the client device 102, and that is included        in the message 400.    -   A message image payload 406: image data captured by a camera        component of a client device 102 or retrieved from memory of a        client device 102, and that is included in the message 400.    -   A message video payload 408: video data captured by a camera        component or retrieved from a memory component of the client        device 102, and that is included in the message 400.    -   A message audio payload 410: audio data captured by a microphone        or retrieved from the memory component of the client device 102,        and that is included in the message 400.    -   Message annotations 412: annotation data (e.g., filters,        stickers, or other enhancements) that represents annotations to        be applied to the message image payload 406, message video        payload 408, or message audio payload 410 of the message 400.    -   A message duration parameter 414: a parameter value indicating,        in seconds, the amount of time for which content of the message        400 (e.g., the message image payload 406, message video payload        408, and message audio payload 410) is to be presented or made        accessible to a user via the messaging client application 104.    -   A message geolocation parameter 416: geolocation data (e.g.,        latitudinal and longitudinal coordinates) associated with the        content payload of the message 400. Multiple message geolocation        parameter 416 values may be included in the payload, with each        of these parameter values being associated with respective        content items included in the content (e.g., a specific image in        the message image payload 406, or a specific video in the        message video payload 408).    -   A message story identifier 418: identifies values identifying        one or more content collections (e.g., “stories”) with which a        particular content item in the message image payload 406 of the        message 400 is associated. For example, multiple images within        the message image payload 406 may each be associated with        multiple content collections using identifier values.    -   A message tag 420: one or more tags, each of which is indicative        of the subject matter of content included in the message        payload. For example, where a particular image included in the        message image payload 406 depicts an animal (e.g., a lion), a        tag value may be included within the message tag 420 that is        indicative of the relevant animal. Tag values may be generated        manually, based on user input, or may be automatically generated        using, for example, image recognition.    -   A message sender identifier 422: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of a user of the client device 102 on        which the message 400 was generated and from which the message        400 was sent.    -   A message receiver identifier 424: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of a user of the client device 102 to        which the message 400 is addressed.

The contents (e.g., values) of the various components of the message 400may be pointers to locations in tables within which content data valuesare stored. For example, an image value in the message image payload 406may be a pointer to (or address of) a location within the image table308. Similarly, values within the message video payload 408 may point todata stored within the video table 310, values stored within the messageannotations 412 may point to data stored in the annotation table 312,values stored within the message story identifier 418 may point to datastored in the story table 306, and values stored within the messagesender identifier 422 and the message receiver identifier 424 may pointto user records stored within the entity table 302.

FIG. 5 is a schematic diagram illustrating an access-limiting process500, in terms of which access to content (e.g., an ephemeral message502, and associated multimedia payload of data) or a content collection(e.g., an ephemeral message story 504) may be time-limited (e.g., madeephemeral).

An ephemeral message 502 is shown to be associated with a messageduration parameter 506, the value of which determines an amount of timethat the ephemeral message 502 will be displayed to a receiving user ofthe ephemeral message 502 by the messaging client application 104. Inone embodiment, where the messaging client application 104 is socialmedia application client, an ephemeral message 502 is viewable by areceiving user for up to a maximum of 10 seconds, depending on theamount of time that the sending user specifies using the messageduration parameter 506.

The message duration parameter 506 and the message receiver identifier424 are shown to be inputs to a message timer 512, which is responsiblefor determining the amount of time that the ephemeral message 502 isshown to a particular receiving user identified by the message receiveridentifier 424. In particular, the ephemeral message 502 will only beshown to the relevant receiving user for a time period determined by thevalue of the message duration parameter 506. The message timer 512 isshown to provide output to a more generalized ephemeral timer system202, which is responsible for the overall timing of display of content(e.g., an ephemeral message 502) to a receiving user.

The ephemeral message 502 is shown in FIG. 5 to be included within anephemeral message story 504 (e.g., a personal story, or an event story).The ephemeral message story 504 has an associated story durationparameter 508, a value of which determines a time duration for which theephemeral message story 504 is presented and accessible to users of themessaging system 100. The story duration parameter 508, for example, maybe the duration of a music concert, where the ephemeral message story504 is a collection of content pertaining to that concert.Alternatively, a user (either the owning user or a curator user) mayspecify the value for the story duration parameter 508 when performingthe setup and creation of the ephemeral message story 504.

Additionally, each ephemeral message 502 within the ephemeral messagestory 504 has an associated story participation parameter 510, a valueof which determines the duration of time for which the ephemeral message502 will be accessible within the context of the ephemeral message story504. Accordingly, a particular ephemeral message 502 may “expire” andbecome inaccessible within the context of the ephemeral message story504, prior to the ephemeral message story 504 itself expiring in termsof the story duration parameter 508. The story duration parameter 508,story participation parameter 510, and message receiver identifier 424each provide input to a story timer 514, which operationally determineswhether a particular ephemeral message 502 of the ephemeral messagestory 504 will be displayed to a particular receiving user and, if so,for how long. Note that the ephemeral message story 504 is also aware ofthe identity of the particular receiving user as a result of the messagereceiver identifier 424.

Accordingly, the story timer 514 operationally controls the overalllifespan of an associated ephemeral message story 504, as well as anindividual ephemeral message 502 included in the ephemeral message story504. In one embodiment, each and every ephemeral message 502 within theephemeral message story 504 remains viewable and accessible for a timeperiod specified by the story duration parameter 508. In a furtherembodiment, a certain ephemeral message 502 may expire, within thecontext of the ephemeral message story 504, based on a storyparticipation parameter 510. Note that a message duration parameter 506may still determine the duration of time for which a particularephemeral message 502 is displayed to a receiving user, even within thecontext of the ephemeral message story 504. Accordingly, the messageduration parameter 506 determines the duration of time that a particularephemeral message 502 is displayed to a receiving user, regardless ofwhether the receiving user is viewing that ephemeral message 502 insideor outside the context of an ephemeral message story 504.

The ephemeral timer system 202 may furthermore operationally remove aparticular ephemeral message 502 from the ephemeral message story 504based on a determination that it has exceeded an associated storyparticipation parameter 510. For example, when a sending user hasestablished a story participation parameter 510 of 24 hours fromposting, the ephemeral timer system 202 will remove the relevantephemeral message 502 from the ephemeral message story 504 after thespecified 24 hours. The ephemeral timer system 202 also operates toremove an ephemeral message story 504 either when the storyparticipation parameter 510 for each and every ephemeral message 502within the ephemeral message story 504 has expired, or when theephemeral message story 504 itself has expired in terms of the storyduration parameter 508.

In certain use cases, a creator of a particular ephemeral message story504 may specify an indefinite story duration parameter 508. In thiscase, the expiration of the story participation parameter 510 for thelast remaining ephemeral message 502 within the ephemeral message story504 will determine when the ephemeral message story 504 itself expires.In this case, a new ephemeral message 502, added to the ephemeralmessage story 504, with a new story participation parameter 510,effectively extends the life of an ephemeral message story 504 to equalthe value of the story participation parameter 510.

In response to the ephemeral timer system 202 determining that anephemeral message story 504 has expired (e.g., is no longer accessible),the ephemeral timer system 202 communicates with the messaging system100 (e.g., specifically, the messaging client application 104) to causean indicium (e.g., an icon) associated with the relevant ephemeralmessage story 504 to no longer be displayed within a user interface ofthe messaging client application 104. Similarly, when the ephemeraltimer system 202 determines that the message duration parameter 506 fora particular ephemeral message 502 has expired, the ephemeral timersystem 202 causes the messaging client application 104 to no longerdisplay an indicium (e.g., an icon or textual identification) associatedwith the ephemeral message 502.

FIG. 6 shows an example entity recognition system 210, according to someexample embodiments. To avoid obscuring the inventive subject matterwith unnecessary detail, various functional components (e.g., modulesand engines) that are not germane to conveying an understanding of theinventive subject matter have been omitted from FIG. 6. However, askilled artisan will readily recognize that various additionalfunctional components may be supported by the entity recognition system210 to facilitate additional functionality that is not specificallydescribed herein.

As is understood by skilled artisans in the relevant computer arts, eachfunctional component (e.g., engine) illustrated in FIG. 6 may beimplemented using hardware (e.g., a processor of a machine) or acombination of logic (e.g., executable software instructions) andhardware (e.g., memory and processor of a machine) for executing thelogic. Furthermore, the various functional components depicted in FIG. 6may reside on a single computer (e.g., a laptop), or may be distributedacross several computers in various arrangements such as cloud-basedarchitectures. Moreover, any two or more modules of the entityrecognition system 210 may be combined into a single module, orsubdivided among multiple modules. It shall be appreciated that whilethe functional components (e.g., engines) of FIG. 6 are discussed in thesingular sense, in other embodiments, multiple instances of one or moreof the modules may be employed.

As illustrated, the entity recognition system 210 comprises an interfaceengine 605, a recognition engine 610, and a content engine 615. Theinterface engine 605 is configured to identify input data, such as amultimodal message (e.g., social media post) generated by application114. The recognition engine 610 is configured to process data in theinput data (e.g., one or more terms in a text caption, an image) toidentify a named entity in the input data. The content engine 645 isconfigured to select overlay content that has been pre-associated withan identified entity. In some example embodiments, the identified entityis transmitted to the annotation system 206 for further processing(e.g., further annotation of an ephemeral message).

FIG. 7 shows a flow diagram of an example method 700 for implementingmultimodal named entity recognition, according to some exampleembodiments. At operation 705, the interface engine 605 identifies amultimodal message. For example, at operation 705, the interface engine605 identifies a social media post containing an image with a captionthat describes image. At operation 710, the recognition engine 610generates an image embedding. For example, at operation 710, therecognition engine 610 generates an image embedding using aconvolutional neural network. At operation 715, the recognition engine610 generates a word embedding from words in the caption, as discussedin further detail below with reference to FIG. 9. At operation 720, therecognition engine 610 generates character embedding from characters inthe words of the caption identified at operation 705, as discussed infurther below with reference to FIG. 9.

At operation 730, the recognition engine 610 generates a combinedembedding from the image embedding, the word embedding, and thecharacter embedding generated at operations 710-720. In some exampleembodiments, at operation 730, the recognition engine 610 implements abidirectional recurrent neural network that is trained to placeattention or emphasis on embedding modalities that are most relevant tothe subject matter of the multimodal message, as discussed in furtherdetail below with reference to FIG. 9. At operation 735, the recognitionengine 610 identifies a named entity in the words of the caption of themultimodal message. For example, at operation 735, the recognitionengine 610 determines that one or more terms of the caption correspondto a multiword city (e.g., New York City).

At operation 740, the content engine 615 identifies content associatedwith the identified named entity. For example, at operation 740, thecontent engine 615 identifies one or more items of overlay content(e.g., user interface content, images, stickers, cartoon avatars, etc.)that is pre-associated with the identified named entity, as discussed infurther detail below with reference to FIGS. 10 and 11. At operation745, the interface engine 605 transmits the multimodal message and theidentified content. For example, at operation 745, the interface engine605 publishes the identified content and the multimodal message as anephemeral message 502.

FIG. 8 shows two example multimodal messages 800 and 805, according tosome example embodiments. As discussed, several challenges remain forrecognizing named entities from extremely short and coarse text found insocial media posts. In particular, short social media posts often do notprovide enough textual context to resolve polysemous entities. Forexample, a multimodal message 800 includes textual context caption 805A(“monopoly is da best”), character context data 805B (characters fromthe words in the caption), visual context data 805C (an image, such asan image of a board game). An NER scheme can have difficulty processingmultimodal message 800 because one or more terms in the caption can bepolysemous and have different meanings. For example, the term “monopoly”in textual context caption 805A may refer to a board game (named entity)or a term in economics. To this end, the recognition engine 610 canprocess message 800 to generate modality attention item 807 whichemphasizes the visual context data 805C.

An additional challenge stems from noisy misspelled text in messages.Social media posts can create a huge number of unknown word tokens(e.g., embeddings) due to inconsistent lexical notations and frequentmentions of various newly trending entities. For example, multimodalmessage 810 includes textual context caption 815A (“xoxoMarshmelloooo”), and character context data 815B (characters from thewords in the caption). An NER scheme can have difficulty processingmultimodal message 810 because “Marshmelloooo” is a misspelling of anamed entity: “Marshmello” (a music producer). To this end, therecognition engine 610 can process message 810 to generate modalityattention item 813 which attenuates the word-level signal for unknownword tokens (e.g. “Marshmellooooo” with trailing ‘o’s) and amplifiescharacter-level features instead (e.g. capitalized first letter, lexicalsimilarity to other known named entity token ‘Marshmello’, etc.),thereby suppressing noise information (e.g., unknown word (UNK) tokenembedding) in decoding steps. In this way, the recognition engine 610avoids naive concatenation of mode data, which is vulnerable to noisysocial media posts.

FIG. 9 shows an example recognition engine 610, according to someexample embodiments. As illustrated, the recognition engine 610 is anentity neural network comprising a feature module 900, attention module925, hybrid module 950 which produces output data 965. The belowdiscussion adopts the following notations: Let x={x_(t)}_(t=1) ^(T) asequence of input tokens with length T, with a corresponding labelsequence y={y_(t)}_(t=1) ^(T) indicating named entities (e.g. instandard BIO formats). Each input token is composed of three modalities:x_(t)={x_(t) ^((w)), x_(t) ^((c)), x_(t) ^((v))} for word embeddings,character embeddings, and visual embeddings representations,respectively.

Word Embeddings: With reference to the word embedding unit 910 of thefeature module 900, word embeddings are obtained from an unsupervisedlearning model that learns co-occurrence statistics of words from alarge external corpus, according to some example embodiments. Thetrained model learns word embeddings as distributional semantics ofwords. In some example embodiments, the visual embedding unit 905implements pre-trained embeddings from Stanford GLobal Vectors for WordRepresentations (GloVE) model.

Character Embeddings: With reference to the character embeddings unit915 of the feature module 900, character embeddings are obtained from aBi-LSTM which takes as input a sequence of characters of each token,according to some example embodiments. In other example alternativeembodiments, character embeddings unit 915 generates characterembeddings using a convolutional neural network.

Visual embeddings: In some example embodiments, the visual embeddingunit 905 implements a modified Inception model (GoogLeNet) (Szegedy etal., 2014, 2015) trained on an ImageNet dataset (Russakovsky et al.,2015) to classify multiple objects in the scene. The modified Inceptionmodel comprises deep 22 layers, training of which can be performedutilizing “network in network” principles and dimensional reduction toimprove computing resource utilization. The final layer representationencodes discriminative information describing what objects are shown inan image, which provide auxiliary contexts for understanding textualtokens and entities in accompanying captions. In some exampleembodiments, the visual embeddings are input into the NER decoder (e.g.,hybrid module 950) at every step, where each step corresponds to a groupof characters separated from other characters by spaces (e.g., each stepcorresponds to a word from a group of words that form a sentence).

In some example embodiments, each embedding (e.g., character embedding,word embedding, visual/image embedding) is input into a transform layerbefore transfer to the attention module (e.g., a transform layer of theform x_(t) ^((w)); x_(t) ^((c)); x_(t) ^((v)):=σ_(w)(x_(t) ^((w)));σ_(c)(x_(t) ^((c))); σ_(v)(x_(t) ^((v))).

The attention module 925 includes a modality attention unit 935 isconfigured to learn a unified representation space for multipleavailable modalities (e.g. words, characters, images, etc.), and producea single vector representation (e.g., input token 940) with aggregatedknowledge among multiple modalities, based on their weighted importance,according to some example embodiments.

In some example embodiments, the word and character level contexts arecombined by concatenating the word and character embeddings at eachdecoding step, e.g. h_(t)=LSTM([x_(t) ^((w)); x_(t) ^((c))]). However,this naive concatenation of two modalities (i.e., word and characters)may result in inaccurate decoding specifically for unknown word tokenembeddings (e.g. an all zero vector x_(t) ^((w))=0 or a random vectorx^(t(w))=∈˜U(−σ, +σ) is assigned for any unknown token x^(t), thush^(t)=LSTM([0; x_(t) ^((c))]) or LSTM([∈; x_(t) ^((c))])). While thisconcatenation approach may not yield significant errors for standardizeddatasets, it can induce performance degradation when applied to socialmedia posts datasets which contain a significant number of missingtokens (e.g., an UNK token).

In some example embodiments, naive merging of textual and visualinformation (e.g. h, =LSTM([x_(t) ^((w)); x_(t) ^((c)); x_(t) ^((v))]))is performed. However, this approach can yield suboptimal results aseach modality is treated equally informative, when in some messages theimage is irrelevant and not informative.

In some example embodiments, the modality attention unit 930 isconfigured to adaptively attenuate or emphasize each modality (e.g.,attenuate or emphasize each of the embeddings in input embedding 930) asa whole at each decoding step t, and produce a soft attended contextvector x_(t) as an input token 940 for processing by the hybrid module950. In some example embodiments, the modality attention unit 930 isconfigured as follows:

[a_(t)^((w)); a_(t)^((c)); a_(t)^((v))] = σ(W_(m) ⋅ [x_(t)^((w)); x_(t)^((c)); x_(t)^((v))] + b_(m))$\alpha_{t}^{(m)} = {{\frac{\exp\mspace{11mu}\left( a_{t}^{(m^{\prime})} \right)}{\sum_{m^{\prime} \in {\{{w,c,v}\}}}{\exp\mspace{11mu}\left( a_{t}^{(m^{\prime})} \right)}}\text{∀}m} \in \left\{ {w,c,v} \right\}}$

where α_(t)=[α_(t) ^((w)); α_(t) ^((c)); α_(t) ^((v))]∈R³ is anattention vector at each decoding step t, and

is a final context vector at t that maximizes information gain forx_(t). Note that the optimization of the objective function withmodality attention requires each modality to have the same dimension(e.g., x_(t) ^((w)), x_(t) ^((c)), x_(t) ^((v))∈R^(p)), and that thetransformation via W_(m) enforces each modality to be mapped into thesame unified subspace, where the weighted average of which encodesdistinguishing features for recognition of named entities.

When visual context is not provided with each token (e.g., a socialmedia post with a caption but no accompanying image), the modalityattention unit 935 applies the following scheme:

[a_(t)^((w)); a_(t)^((c))] = σ(W_(m) ⋅ [x_(t)^((w)); x_(t)^((c))] + b_(m))$\alpha_{t}^{(m)} = {{\frac{\exp\mspace{11mu}\left( a_{t}^{(m)} \right)}{\sum_{m^{\prime} \in {\{{w,c}\}}}{\exp\mspace{11mu}\left( a_{t}^{(m^{\prime})} \right)}}\text{∀}m} \in \left\{ {w,c} \right\}}$${\overset{\_}{x}}_{t} = {\sum\limits_{m \in {\{{w,c}\}}}^{\;}{\alpha_{t}^{(m)}x_{t}^{(m)}}}$

The hybrid module 950 includes a Bi-LSTM 960 and which inputs into a CRF955, which operate over steps, t. In particular,

i_(t) = σ(W_(xi)h_(t − 1) + W_(ci)c_(t − 1))$c_{t} = {{\left( {1 - i_{t}} \right)\mspace{11mu}{XNOR}\mspace{14mu} c_{t - 1}} + {i_{t}\mspace{14mu}{XNOR}\mspace{11mu}{\tanh\left( {{W_{xc}\overset{\_}{x_{t}}} + {W_{hc}h_{t - 1}}} \right)}}}$$\sigma_{t} = {\sigma\left( {{W_{xo}\overset{\_}{x_{t}}} + {W_{ho}h_{t - 1}} + {W_{co}c_{t}}} \right)}$$h_{t} = {{{LSTM}\left( \overset{\_}{x_{t}} \right)} = {\sigma_{t}\mspace{14mu}{XNOR}\mspace{11mu}{\tanh\left( c_{t} \right)}}}$

where x_(t) is a weighted average of three modalities x_(t)={x_(t)^((w)); x_(t) ^((c)); x_(t) ^((v))} via the modality attention module935. In some example embodiments, bias terms for gates are omitted forsimplicity of formulation.

In some exam embodiments, a bidirectional entity token representation

=[

;

] is obtained by concatenating its left and right contextrepresentations. To enforce structural correlations between labels insequence decoding,

is then passed to a conditional random field (CRF) to produce a labelfor each token maximizing the following objective.

$y*={{argmax}_{y}\mspace{11mu}{p\left( {{y❘\overset{\leftrightarrow}{h_{t}}};W_{CRF}} \right)}}$${p\left( {{y❘\overset{\leftrightarrow}{h_{t}}};W_{CRF}} \right)} = \frac{\prod_{t}^{\;}{\psi_{t}\left( {y_{t - 1},y_{t},\overset{\leftrightarrow}{h_{t}}} \right)}}{\sum_{y^{\prime}}{\prod_{t}{\psi_{t}\left( {{{y^{\prime}}_{t - 1}{y^{\prime}}_{t}},\overset{\leftrightarrow}{h_{t}}} \right)}}}$

where ψ_(t)(y_(t-1), y_(t),

) is a potential function, W_(CRF) is a set of parameters that definesthe potential functions and weight vectors for label pairs (y′, y′).Bias terms are omitted for brevity of formulation.

The model can be trained via log likelihood maximization for thetraining set {(x_(i), y_(i))}:

L(W _(CRF))=Σ_(i) p(y|

;W _(CRF))

FIG. 10 shows an example multimodal message 1000, according to someexample embodiments. As illustrated, the multimodal message includes animage 1003, and a caption 1015 that includes one or more words thatdescribe what is going on in the multimodal message 1000. For example,the image 1003 is of an example subject 1005 (e.g., a fictionalcelebrity/singer London Milton) on a concert stage in front of anaudience 1010, and the user (not depicted) has input the caption 1015with the words “London Live!!!!<3<3<3” as an overlay. Conventionalinformation extraction schemes can struggle to identify the named entityin the caption 1015 due to the term ambiguity. For example, the usercould be saying that they are at a restaurant called “London Live”, orthat they are currently in the city of London, that they intent to livein the city of London (i.e., in the case where intended caption was“London, to live!!!!” but typos occurred), that they are viewing a liveshow of the singer/celebrity London Milton on stage, and so on.

FIG. 11 shows an example modified multimodal message 1100 generatedusing the system 210, according to some example embodiments. Inparticular, the system 210 have placed greater emphasis on the imagemodality (e.g., the input token 878 places emphasis on image informationdepicting a concert) to identify that the named entity in the caption1015 is London Milton, the singer/celebrity. In response to recognizingthat London Milton is the name entity, one or more items of overlaycontent 1105 can be recommended to the user, who then may overlay theoverlay content 1105 on the multimodal message 1100, which can then bepublished as an ephemeral message 502. In some example embodiments, eachof the potential entities that can be output as the named entity of amultimodal message is pre-associated with overlay content. In thoseexample embodiments, when an entity is identified as the likely subjectof a multimodal message, the pre-associated overlay content (e.g.,overlay content 1105) can be used to create a modified multimodalmessage to be published on a social network or otherwise stored tomemory of a client device.

FIG. 12 is a block diagram illustrating an example software architecture1206, which may be used in conjunction with various hardwarearchitectures herein described. FIG. 12 is a non-limiting example of asoftware architecture, and it will be appreciated that many otherarchitectures may be implemented to facilitate the functionalitydescribed herein. The software architecture 1206 may execute on hardwaresuch as a machine 1120 of FIG. 11 that includes, among other things,processors, memory, and input/output (I/O) components. A representativehardware layer 1252 is illustrated and can represent, for example, themachine 1120 of FIG. 11. The representative hardware layer 1252 includesa processing unit 1254 having associated executable instructions 1204.The executable instructions 1204 represent the executable instructionsof the software architecture 1206, including implementation of themethods, components, and so forth described herein. The hardware layer1252 also includes a memory/storage 1256, which also has the executableinstructions 1204. The hardware layer 1252 may also comprise otherhardware 1258.

In the example architecture of FIG. 12, the software architecture 1206may be conceptualized as a stack of layers where each layer providesparticular functionality. For example, the software architecture 1206may include layers such as an operating system 1202, libraries 1220,frameworks/middleware 1218, applications 1216, and a presentation layer1211. Operationally, the applications 1216 and/or other componentswithin the layers may invoke API calls 1208 through the software stackand receive a response in the form of messages 1212. The layersillustrated are representative in nature and not all softwarearchitectures have all layers. For example, some mobile orspecial-purpose operating systems may not provide aframeworks/middleware 1218, while others may provide such a layer. Othersoftware architectures may include additional or different layers.

The operating system 1202 may manage hardware resources and providecommon services. The operating system 1202 may include, for example, akernel 1222, services 1224, and drivers 1226. The kernel 1222 may act asan abstraction layer between the hardware and the other software layers.For example, the kernel 1222 may be responsible for memory management,processor management (e.g., scheduling), component management,networking, security settings, and so on. The services 1224 may provideother common services for the other software layers. The drivers 1226are responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 1226 include display drivers, cameradrivers, Bluetooth@ drivers, flash memory drivers, serial communicationdrivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi® drivers,audio drivers, power management drivers, and so forth depending on thehardware configuration.

The libraries 1220 provide a common infrastructure that is used by theapplications 1216 and/or other components and/or layers. The libraries1220 provide functionality that allows other software components toperform tasks in an easier fashion than by interfacing directly with theunderlying operating system 1202 functionality (e.g., kernel 1222,services 1224, and/or drivers 1226). The libraries 1220 may includesystem libraries 1244 (e.g., C standard library) that may providefunctions such as memory allocation functions, string manipulationfunctions, mathematical functions, and the like. In addition, thelibraries 1220 may include API libraries 1246 such as media libraries(e.g., libraries to support presentation and manipulation of variousmedia formats such as MPEG4, H.264, MP3, AAC, AMR, JPG, or PNG),graphics libraries (e.g., an OpenGL framework that may be used to render2D and 3D graphical content on a display), database libraries (e.g.,SQLite that may provide various relational database functions), weblibraries (e.g., WebKit that may provide web browsing functionality),and the like. The libraries 1220 may also include a wide variety ofother libraries 1248 to provide many other APIs to the applications 1216and other software components/modules.

The frameworks/middleware 1218 provide a higher-level commoninfrastructure that may be used by the applications 1216 and/or othersoftware components/modules. For example, the frameworks/middleware 1218may provide various graphic user interface (GUI) functions, high-levelresource management, high-level location services, and so forth. Theframeworks/middleware 1218 may provide a broad spectrum of other APIsthat may be utilized by the applications 1216 and/or other softwarecomponents/modules, some of which may be specific to a particularoperating system 1202 or platform.

The applications 1216 include built-in applications 1238 and/orthird-party applications 1240. Examples of representative built-inapplications 1238 may include, but are not limited to, a contactsapplication, a browser application, a book reader application, alocation application, a media application, a messaging application,and/or a game application. The third-party applications 1240 may includean application developed using the ANDROID™ or IOS™ software developmentkit (SDK) by an entity other than the vendor of the particular platform,and may be mobile software running on a mobile operating system such asIOS™, ANDROID™, WINDOWS® Phone, or other mobile operating systems. Thethird-party applications 1240 may invoke the API calls 1208 provided bythe mobile operating system (such as the operating system 1202) tofacilitate functionality described herein.

The applications 1216 may use built-in operating system functions (e.g.,kernel 1222, services 1224, and/or drivers 1226), libraries 1220, andframeworks/middleware 1218 to create user interfaces to interact withusers of the system. Alternatively, or additionally, in some systems,interactions with a user may occur through a presentation layer, such asthe presentation layer 1211. In these systems, the application/component“logic” can be separated from the aspects of the application/componentthat interact with a user.

FIG. 13 is a block diagram illustrating components of a machine 1300,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.Specifically, FIG. 13 shows a diagrammatic representation of the machine1300 in the example form of a computer system, within which instructions1316 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 1300 to perform any oneor more of the methodologies discussed herein may be executed. As such,the instructions 1316 may be used to implement modules or componentsdescribed herein. The instructions 1316 transform the general,non-programmed machine 1300 into a particular machine 1300 programmed tocarry out the described and illustrated functions in the mannerdescribed. In alternative embodiments, the machine 1300 operates as astandalone device or may be coupled (e.g., networked) to other machines.In a networked deployment, the machine 1300 may operate in the capacityof a server machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 1300 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a set-top box (STB), apersonal digital assistant (PDA), an entertainment media system, acellular telephone, a smartphone, a mobile device, a wearable device(e.g., a smart watch), a smart home device (e.g., a smart appliance),other smart devices, a web appliance, a network router, a networkswitch, a network bridge, or any machine capable of executing theinstructions 1316, sequentially or otherwise, that specify actions to betaken by the machine 1300. Further, while only a single machine 1300 isillustrated, the term “machine” shall also be taken to include acollection of machines that individually or jointly execute theinstructions 1316 to perform any one or more of the methodologiesdiscussed herein.

The machine 1300 may include processors 1310, memory/storage 1330, andI/O components 1350, which may be configured to communicate with eachother such as via a bus 1302. The memory/storage 1330 may include amemory 1332, such as a main memory, or other memory storage, and astorage unit 1336, both accessible to the processors 1310 such as viathe bus 1302. The storage unit 1336 and memory 1332 store theinstructions 1316 embodying any one or more of the methodologies orfunctions described herein. The instructions 1316 may also reside,completely or partially, within the memory 1332, within the storage unit1336, within at least one of the processors 1310 (e.g., within theprocessor cache memory accessible to processor units 1312 or 1314), orany suitable combination thereof, during execution thereof by themachine 1300. Accordingly, the memory 1332, the storage unit 1336, andthe memory of the processors 1310 are examples of machine-readablemedia.

The I/O components 1350 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 1350 that are included in a particular machine 1300 willdepend on the type of machine. For example, portable machines such asmobile phones will likely include a touch input device or other suchinput mechanisms, while a headless server machine will likely notinclude such a touch input device. It will be appreciated that the I/Ocomponents 1350 may include many other components that are not shown inFIG. 13. The I/O components 1350 are grouped according to functionalitymerely for simplifying the following discussion and the grouping is inno way limiting. In various example embodiments, the I/O components 1350may include output components 1352 and input components 1354. The outputcomponents 1352 may include visual components (e.g., a display such as aplasma display panel (PDP), a light-emitting diode (LED) display, aliquid-crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), haptic components (e.g., avibratory motor, resistance mechanisms), other signal generators, and soforth. The input components 1354 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or other pointinginstruments), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

In further example embodiments, the I/O components 1350 may includebiometric components 1356, motion components 1358, environmentcomponents 1360, or position components 1362 among a wide array of othercomponents. For example, the biometric components 1356 may includecomponents to detect expressions (e.g., hand expressions, facialexpressions, vocal expressions, body gestures, or eye tracking), measurebiosignals (e.g., blood pressure, heart rate, body temperature,perspiration, or brain waves), identify a person (e.g., voiceidentification, retinal identification, facial identification,fingerprint identification, or electroencephalogram-basedidentification), and the like. The motion components 1358 may includeacceleration sensor components (e.g., accelerometer), gravitation sensorcomponents, rotation sensor components (e.g., gyroscope), and so forth.The environment components 1360 may include, for example, illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometers that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gassensors to detect concentrations of hazardous gases for safety or tomeasure pollutants in the atmosphere), or other components that mayprovide indications, measurements, or signals corresponding to asurrounding physical environment. The position components 1362 mayinclude location sensor components (e.g., a GPS receiver component),altitude sensor components (e.g., altimeters or barometers that detectair pressure from which altitude may be derived), orientation sensorcomponents (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1350 may include communication components 1364operable to couple the machine 1300 to a network 1380 or devices 1370via a coupling 1382 and a coupling 1372, respectively. For example, thecommunication components 1364 may include a network interface componentor other suitable device to interface with the network 1380. In furtherexamples, the communication components 1364 may include wiredcommunication components, wireless communication components, cellularcommunication components, near field communication (NFC) components,Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components,and other communication components to provide communication via othermodalities. The devices 1370 may be another machine or any of a widevariety of peripheral devices (e.g., a peripheral device coupled via aUSB).

Moreover, the communication components 1364 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 1364 may include radio frequency identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional barcodes such as Universal Product Code (UPC) barcode,multi-dimensional barcodes such as Quick Response (QR) code, Aztec code,Data Matrix, Dataglyph, MaxiCode, PDF418, Ultra Code, UCC RSS-2Dbarcode, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components1364, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

Glossary

“CARRIER SIGNAL” in this context refers to any intangible medium that iscapable of storing, encoding, or carrying instructions 1316 forexecution by the machine 1300, and includes digital or analogcommunications signals or other intangible media to facilitatecommunication of such instructions 1316. Instructions 1316 may betransmitted or received over the network 1380 using a transmissionmedium via a network interface device and using any one of a number ofwell-known transfer protocols.

“CLIENT DEVICE” in this context refers to any machine 1300 thatinterfaces to a communications network 1380 to obtain resources from oneor more server systems or other client devices 102. A client device 102may be, but is not limited to, a mobile phone, desktop computer, laptop,PDA, smartphone, tablet, ultrabook, netbook, multi-processor system,microprocessor-based or programmable consumer electronics system, gameconsole, set-top box, or any other communication device that a user mayuse to access a network 1380.

“COMMUNICATIONS NETWORK” in this context refers to one or more portionsof a network 1380 that may be an ad hoc network, an intranet, anextranet, a virtual private network (VPN), a local area network (LAN), awireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), ametropolitan area network (MAN), the Internet, a portion of theInternet, a portion of the Public Switched Telephone Network (PSTN), aplain old telephone service (POTS) network, a cellular telephonenetwork, a wireless network, a Wi-Fi® network, another type of network,or a combination of two or more such networks. For example, a network ora portion of a network 1380 may include a wireless or cellular networkand the coupling may be a Code Division Multiple Access (CDMA)connection, a Global System for Mobile communications (GSM) connection,or another type of cellular or wireless coupling. In this example, thecoupling may implement any of a variety of types of data transfertechnology, such as Single Carrier Radio Transmission Technology(1×RTT), Evolution-Data Optimized (EVDO) technology, General PacketRadio Service (GPRS) technology, Enhanced Data rates for GSM Evolution(EDGE) technology, third Generation Partnership Project (3GPP) including3G, fourth generation wireless (4G) networks, Universal MobileTelecommunications System (UMTS), High-Speed Packet Access (HSPA),Worldwide Interoperability for Microwave Access (WiMAX), Long-TermEvolution (LTE) standard, others defined by various standard-settingorganizations, other long-range protocols, or other data transfertechnology.

“EPHEMERAL MESSAGE” in this context refers to a message 400 that isaccessible for a time-limited duration. An ephemeral message 502 may bea text, an image, a video, and the like. The access time for theephemeral message 502 may be set by the message sender. Alternatively,the access time may be a default setting or a setting specified by therecipient. Regardless of the setting technique, the message 400 istransitory.

“MACHINE-READABLE MEDIUM” in this context refers to a component, adevice, or other tangible media able to store instructions 1316 and datatemporarily or permanently and may include, but is not limited to,random-access memory (RAM), read-only memory (ROM), buffer memory, flashmemory, optical media, magnetic media, cache memory, other types ofstorage (e.g., erasable programmable read-only memory (EPROM)), and/orany suitable combination thereof. The term “machine-readable medium”should be taken to include a single medium or multiple media (e.g., acentralized or distributed database, or associated caches and servers)able to store instructions 1316. The term “machine-readable medium”shall also be taken to include any medium, or combination of multiplemedia, that is capable of storing instructions 1316 (e.g., code) forexecution by a machine 1300, such that the instructions 1316, whenexecuted by one or more processors 1310 of the machine 1300, cause themachine 1300 to perform any one or more of the methodologies describedherein. Accordingly, a “machine-readable medium” refers to a singlestorage apparatus or device, as well as “cloud-based” storage systems orstorage networks that include multiple storage apparatus or devices. Theterm “machine-readable medium” excludes signals per se.

“COMPONENT” in this context refers to a device, a physical entity, orlogic having boundaries defined by function or subroutine calls, branchpoints, APIs, or other technologies that provide for the partitioning ormodularization of particular processing or control functions. Componentsmay be combined via their interfaces with other components to carry outa machine process. A component may be a packaged functional hardwareunit designed for use with other components and a part of a program thatusually performs a particular function of related functions. Componentsmay constitute either software components (e.g., code embodied on amachine-readable medium) or hardware components. A “hardware component”is a tangible unit capable of performing certain operations and may beconfigured or arranged in a certain physical manner. In various exampleembodiments, one or more computer systems (e.g., a standalone computersystem, a client computer system, or a server computer system) or one ormore hardware components of a computer system (e.g., a processor 1312 ora group of processors 1310) may be configured by software (e.g., anapplication or application portion) as a hardware component thatoperates to perform certain operations as described herein. A hardwarecomponent may also be implemented mechanically, electronically, or anysuitable combination thereof. For example, a hardware component mayinclude dedicated circuitry or logic that is permanently configured toperform certain operations. A hardware component may be aspecial-purpose processor, such as a field-programmable gate array(FPGA) or an application-specific integrated circuit (ASIC). A hardwarecomponent may also include programmable logic or circuitry that istemporarily configured by software to perform certain operations. Forexample, a hardware component may include software executed by ageneral-purpose processor or other programmable processor. Onceconfigured by such software, hardware components become specificmachines (or specific components of a machine 1300) uniquely tailored toperform the configured functions and are no longer general-purposeprocessors 1310. It will be appreciated that the decision to implement ahardware component mechanically, in dedicated and permanently configuredcircuitry, or in temporarily configured circuitry (e.g., configured bysoftware) may be driven by cost and time considerations. Accordingly,the phrase “hardware component” (or “hardware-implemented component”)should be understood to encompass a tangible entity, be that an entitythat is physically constructed, permanently configured (e.g.,hardwired), or temporarily configured (e.g., programmed) to operate in acertain manner or to perform certain operations described herein.

Considering embodiments in which hardware components are temporarilyconfigured (e.g., programmed), each of the hardware components need notbe configured or instantiated at any one instance in time. For example,where a hardware component comprises a general-purpose processor 1312configured by software to become a special-purpose processor, thegeneral-purpose processor 1312 may be configured as respectivelydifferent special-purpose processors (e.g., comprising differenthardware components) at different times. Software accordingly configuresa particular processor 1312 or processors 1310, for example, toconstitute a particular hardware component at one instance of time andto constitute a different hardware component at a different instance oftime.

Hardware components can provide information to, and receive informationfrom, other hardware components. Accordingly, the described hardwarecomponents may be regarded as being communicatively coupled. Wheremultiple hardware components exist contemporaneously, communications maybe achieved through signal transmission (e.g., over appropriate circuitsand buses) between or among two or more of the hardware components. Inembodiments in which multiple hardware components are configured orinstantiated at different times, communications between or among suchhardware components may be achieved, for example, through the storageand retrieval of information in memory structures to which the multiplehardware components have access. For example, one hardware component mayperform an operation and store the output of that operation in a memorydevice to which it is communicatively coupled. A further hardwarecomponent may then, at a later time, access the memory device toretrieve and process the stored output. Hardware components may alsoinitiate communications with input or output devices, and can operate ona resource (e.g., a collection of information).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors 1310 that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors 1310 may constitute processor-implementedcomponents that operate to perform one or more operations or functionsdescribed herein. As used herein, “processor-implemented component”refers to a hardware component implemented using one or more processors1310. Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor 1312 or processors1310 being an example of hardware. For example, at least some of theoperations of a method may be performed by one or more processors 1310or processor-implemented components. Moreover, the one or moreprocessors 1310 may also operate to support performance of the relevantoperations in a “cloud computing” environment or as a “software as aservice” (SaaS). For example, at least some of the operations may beperformed by a group of computers (as examples of machines 1300including processors 1310), with these operations being accessible via anetwork 1380 (e.g., the Internet) and via one or more appropriateinterfaces (e.g., an API). The performance of certain of the operationsmay be distributed among the processors 1310, not only residing within asingle machine 1300, but deployed across a number of machines 1300. Insome example embodiments, the processors 1310 or processor-implementedcomponents may be located in a single geographic location (e.g., withina home environment, an office environment, or a server farm). In otherexample embodiments, the processors 1310 or processor-implementedcomponents may be distributed across a number of geographic locations.

“PROCESSOR” in this context refers to any circuit or virtual circuit (aphysical circuit emulated by logic executing on an actual processor1312) that manipulates data values according to control signals (e.g.,“commands,” “op codes,” “machine code,” etc.) and which producescorresponding output signals that are applied to operate a machine 1300.A processor may, for example, be a central processing unit (CPU), areduced instruction set computing (RISC) processor, a complexinstruction set computing (CISC) processor, a graphics processing unit(GPU), a digital signal processor (DSP), an ASIC, a radio-frequencyintegrated circuit (RFIC), or any combination thereof. A processor 1310may further be a multi-core processor 1310 having two or moreindependent processors 1312, 1314 (sometimes referred to as “cores”)that may execute instructions 1316 contemporaneously.

“TIMESTAMP” in this context refers to a sequence of characters orencoded information identifying when a certain event occurred, forexample giving date and time of day, sometimes accurate to a smallfraction of a second.

What is claimed is:
 1. A method comprising: identifying a multimodalmessage comprising an image and a string, the string comprising one ormore words; generating, using an entity neural network, an indicationthat at least one of the one or more words is a named entity, the entityneural network comprising an attention neural network trained toincrease emphasis on one of a plurality of embeddings based on relevanceto the multimodal message, the plurality of embeddings comprising animage embedding from the image and a string embedding corresponding tothe string in the multimodal message; and storing, using one or moreprocessors of a machine, the named entity as being associated with themultimodal message.
 2. The method of claim 1, wherein the attentionneural network is trained on a plurality of multimodal messages, eachmultimodal message in the plurality of multimodal messages including atraining caption and a training image.
 3. The method of claim 1, whereinthe entity neural network is trained to process image embedding data initerations, wherein sequential iterations correspond to sequential wordsin a training caption.
 4. The method of claim 1, further comprising:generating, at each iteration, a combined embedding from the imageembedding and the string embedding using the attention neural network.5. The method of claim 4, wherein the entity neural network comprises aclassification neural network that processes the combined embeddinggenerated at each iteration by the attention neural network.
 6. Themethod of claim 5, wherein the classification neural network comprises abidirectional recurrent neural network.
 7. The method of claim 6,wherein the classification neural network comprises a conditional randomfield layer that receives data output by the bidirectional recurrentneural network at each iteration.
 8. The method of claim 1, furthercomprising: generating the image embedding using a convolutional neuralnetwork.
 9. The method of claim 1, further comprising: identifying aword embedding corresponding to the one or more words.
 10. The method ofclaim 1, wherein the string comprises one or more characters includingone or more of: one or more punctuation marks, or one or more emojis.11. A system comprising: one or more processors of a machine; and amemory storing instructions that, when executed by the one or moreprocessors, cause the machine to perform operations comprising:identifying a multimodal message comprising an image and a string, thestring comprising one or more words; generating, using an entity neuralnetwork, an indication that at least one of the one or more words is anamed entity, the entity neural network comprising an attention neuralnetwork trained to increase emphasis on one of a plurality of embeddingsbased on relevance to the multimodal message, the plurality ofembeddings comprising an image embedding from the image and a stringembedding corresponding to the string in the multimodal message; andstoring, using one or more processors of a machine, the named entity asbeing associated with the multimodal message.
 12. The system of claim11, wherein the attention neural network is trained on a plurality ofmultimodal messages, each multimodal message in the plurality ofmultimodal messages including a training caption and a training image.13. The system of claim 11, wherein the entity neural network is trainedto process image embedding data in iterations, wherein sequentialiterations correspond to sequential words in a training caption.
 14. Thesystem of claim 11, further comprising: generating, at each iteration, acombined embedding from the image embedding and the string embeddingusing the attention neural network.
 15. The system of claim 14, whereinthe entity neural network comprises a classification neural network thatprocesses the combined embedding generated at each iteration by theattention neural network.
 16. The system of claim 15, wherein theclassification neural network comprises a bidirectional recurrent neuralnetwork.
 17. The system of claim 16, wherein the classification neuralnetwork comprises a conditional random field layer that receives dataoutput by the bidirectional recurrent neural network at each iteration.18. The method of claim 1, further comprising: generating the imageembedding using a convolutional neural network.
 19. A machine-readablestorage device embodying instructions that, when executed by a machine,cause the machine to perform operations comprising: identifying amultimodal message comprising an image and a string, the stringcomprising one or more words; generating, using an entity neuralnetwork, an indication that at least one of the one or more words is anamed entity, the entity neural network comprising an attention neuralnetwork trained to increase emphasis on one of a plurality of embeddingsbased on relevance to the multimodal message, the plurality ofembeddings comprising an image embedding from the image and a stringembedding corresponding to the string in the multimodal message; andstoring, using one or more processors of a machine, the named entity asbeing associated with the multimodal message.
 20. The machine-readablestorage device of claim 19, wherein the attention neural network istrained on a plurality of multimodal messages, each multimodal messagein the plurality of multimodal messages including a training caption anda training image.